Networking

Regularization and Feature Selection for Networked Features

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Executive Summary

In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, for many real world problems, the authors have such prior knowledge about structure relationships among features. For instance, in Microarray analysis where the genes are features, the genes form biological pathways. Such prior knowledge should be incorporated to build a more accurate and interpretable model, especially in applications with high dimensionality and low sample sizes. Towards an efficient incorporation of the structure relationships, they have designed a classification model where they use an undirected graph to capture the relationship of features.

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